We build ML solutions that fit your data and use case: forecasting, classification, recommendation engines, anomaly detection or process automation. We work with your existing data pipelines and tools (Python, TensorFlow, PyTorch, scikit-learn, cloud ML services) and integrate the trained models into your applications via APIs or embedded runtimes. Focus on business value and robustness, not hype.
Monitoring and retraining are part of our delivery so your ML solution stays accurate over time. Where generative layers help—summaries, retrieval or copilots—we combine classical ML with AI consulting, RAG knowledge bases and AI agents. Governance for regulated use cases is covered via EU AI Act consulting.
Get in touch for a free consultation – we outline use cases, data requirements and typical project scope and cost without obligation.
Coaching, pilots and rollout
AI solutions for businesses →Production-grade enterprise AI
Microsoft Copilot →M365 copilot adoption
FAQ
When you have recurring patterns in your data – forecasting, classification, recommendations, anomaly detection – and off-the-shelf tools are not enough.
We check data and use cases with you first.
From data analysis to a production-ready model often 2–6 months, depending on data availability and complexity.
We deliver in phases so you can validate early.
Your data stays under your control.
We work with you on privacy and quality; training can be on-premise, in your cloud or in isolated environments.
Yes.
We integrate models into your ERP, databases or APIs so predictions or classifications run inside your processes.
Machine-learning development trains and validates models on suitable versioned datasets. Unlike RAG, training changes model parameters, so data splits, leakage controls and reproducible experiments are central.
We assess provenance, target labels, coverage, quality and permitted use. A simple domain or statistical baseline and separate train, validation and test sets are agreed first.
Acceptance uses agreed metrics such as precision, recall, MAE or calibration plus business error cost. MLOps monitors drift, pipeline runs, versions, latency and fallback models.
Sparse examples, biased targets and unstable processes limit validity. Strong offline results do not guarantee production impact. RAG for document knowledge complements this delivery path.
Machine learning development fits learnable patterns in structured data—not open-ended dialogue.
Overview: AI & machine learning (overview). Overview: AI services overview.
Machine learning pays off when enough historical data with a target variable exists and a prediction actually changes decisions.
| Use case | Benefit | Differentiation | Outcome | Next step |
|---|---|---|---|---|
| Forecasts for demand, pricing or failures | Training and evaluation against held-out test data | Not RAG search—no model is trained there | Model with documented quality and a rollback path | AI cost calculator |
| Questions about documents instead of statistical forecasts | Knowledge search with citations is sufficient | No training project with data preparation | Shorter path to value without model operations | AI knowledge base |
| Data situation unclear, target variables missing | Data intake, quality checks and feature analysis first | No model start without a reliable data basis | Feasibility verdict before the investment | Data analytics |
ML models and predictive analytics – overview on AI & machine learning.
Overview: AI & machine learning (overview).
Service overview: AI & machine learning (overview)
Use our funding calculator to see which government grants may apply to your project.
Björn Groenewold – Managing Director
Service cluster
Related services for AI & machine learning: match the service to the need
Quick orientation for AI and machine learning services—from first steps to production solutions, with governance and measurable outcomes.
From the field: KI-Bildverarbeitung für die Qualitätskontrolle (scenario). All references.
Use our interactive calculator for a first budget indication—free and non-binding.
Thorsten Frieling – Projektmanagement
Project references
Documented examples with transparent evidence types — browse matching references or open the full case study.
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